Demand Response (DR) is a utility or electrical provider program to reduce load, on command, by curtailing the use of non-critical end-use loads. Demand Response programs may be deployed to most customer classes. Of particular value are programs that curtail residential Heating, Ventilation and Air Conditioning (HVAC) systems during times of peak usage. This is true since electrical system peaks are often caused by high usage of Residential HVAC systems.
Demand Response programs usually include an electrical load management system that controls the amount of electrical energy used by HVAC loads during peak energy demand periods. The control signals issued by the load management system usually cause electrical load reductions. Such load reductions are commonly referred to in the industry as load shedding. Load shedding is usually the more cost effective alternative to investing in expensive additional power generating capacity. Devices that are used to produce additional power generating capacity at times of high usage are often referred to in the industry as “peakers.”
As of this writing, many utility providers have turned to load shedding as the most viable option to address very high peak demands instead of purchasing peakers. Load shedding usually comprises “direct load control” or demand response programs. Direct load control is a method where utility providers may interrupt the loads of their consumers during critical energy demand times.
Many utilities target Residential and Small Commercial HVAC loads for reduction during peak periods. Two key attributes of a Demand Response program that make this resource more valuable to system operators are as follows: (1) the ability to estimate available load prior to a load control event and (2) to verify load drop during the event.
A typical approach for estimating demand response is to collect the load profile data from an energy meter on non-event days. Then the difference between this baseline load profile and the energy use as measured by the same meter during the event constitutes the demand response reduction realized by that participant. Ideally this difference would be totaled for all of the participants.
Recently, meter reading networks are being deployed to most utility customers, including residential and small commercial customers. Since these Advanced Metering Infrastructure (AMI) networks usually collect meter data from all of the meters for a utility, it is often assumed by utility providers that this data can be used for measuring and verification (M&V).
However, this whole population summation is not practical due to limitations on network meter reading speed. The network bandwidth and architecture of many AMI networks, especially those which use power line carrier (PLC) or RF-based mesh networks, usually will not support retrieving information from the entire population, or a substantial percentage of the entire population, in a short period time, such as over a period of a few hours in order to provide energy analysis after a load shedding event. Usually, data from meters is usually needed within minutes for load drop estimates during a load shedding event. Typically, it takes many hours to receive data from each and every program participant's meter, which could number in the 100's of thousands, within an AMI network.
What is needed in the art is an accurate estimate of the demand response in near real time using data from an AMI network. Collecting information from utility meters over an AMI network may be valuable both prior to a control event to assess potential energy savings as well as during and after a control event to assess delivered energy. A need exists in the art for a method and system that balances the utilization of the network bandwidth against the requirement that information for a demand response event is collected in real time or near real time at granular levels, such as on a sub-hourly or minute-by-minute basis.